Application of a novel deep learning feature extraction framework in  the assessment of intracranial aneurysm rupture risk

Wenlong Li; Miao Song

doi:10.1117/12.3034928

11 July 2024 Application of a novel deep learning feature extraction framework in the assessment of intracranial aneurysm rupture risk

Wenlong Li, Miao Song

Author Affiliations +

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132102Q (2024) https://doi.org/10.1117/12.3034928
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Deep learning has exhibited exceptional effectiveness in predicting the rupture risk of intracranial aneurysms(IA). Our study aimed to provide a valuable tool for extracting Deep Learning features and predicting the rupture risk of intracranial aneurysms. To fulfill this objective, we meticulously developed a deep learning feature extraction framework to extract deep learning features from CTA images and precisely forecast the rupture risk of intracranial aneurysms. The study included 327 IAs in the training dataset and 82 in the test dataset. A model using ResNet-34 demonstrated outstanding performance, achieving an accuracy of 87.65% (80.49%-94.82%), a recall of 98.33% (95.55%-98.74%), and an AUC of 84.52% (76.65%-92.40%). Our results showcase that assessing aneurysm rupture risk performs well when utilizing deep learning features extracted through our developed framework.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenlong Li and Miao Song "Application of a novel deep learning feature extraction framework in the assessment of intracranial aneurysm rupture risk", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132102Q (11 July 2024); https://doi.org/10.1117/12.3034928

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